Advances in Data Mining: Applications and Theoretical Aspects

Theoretical aspects of data mining applications of data mining in multimedia data.- Applications of data mining in marketing and in finance.- Applications of data mining in telecommunication.- Applications of data mining in medicine and agriculture.- Applications of data mining in process control, industry and society.

[1]  F. Sibel Salman,et al.  A mixed-integer programming approach to the clustering problem with an application in customer segmentation , 2006, Eur. J. Oper. Res..

[2]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Iwan von Wartburg,et al.  1 Customer Segmentation Revisited : The Case of the Airline Industry , 2020 .

[4]  P. Chaudhuri.  On a geometric notion of quantiles for multivariate data , 1996 .

[5]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[6]  Dragomir R. Radev,et al.  Centroid-based summarization of multiple documents , 2004, Inf. Process. Manag..

[7]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[8]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[9]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[10]  Benno Stein,et al.  Learning Behavior Models for Hybrid Timed Systems , 2012, AAAI.

[11]  Yi Zhang,et al.  Is it time for a career switch? , 2013, WWW.

[12]  A. Church,et al.  Some properties of conversion , 1936 .

[13]  Izak Benbasat,et al.  E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact , 2007, MIS Q..

[14]  Christophe Osswald,et al.  Understanding the large family of Dempster-Shafer theory's fusion operators - a decision-based measure , 2006, 2006 9th International Conference on Information Fusion.

[15]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[16]  Douglas H. Fisher,et al.  Knowledge Acquisition Via Incremental Conceptual Clustering , 1987, Machine Learning.

[17]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[18]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[19]  Uwe Mönks,et al.  An extended perspective on evidential aggregation rules in machine condition monitoring , 2012, 2012 3rd International Workshop on Cognitive Information Processing (CIP).

[20]  Isabelle Guyon,et al.  Competitive baseline methods set new standards for the NIPS 2003 feature selection benchmark , 2007, Pattern Recognit. Lett..

[21]  Colin de la Higuera,et al.  PAutomaC: a probabilistic automata and hidden Markov models learning competition , 2013, Machine Learning.

[22]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[23]  Wei Li,et al.  New parallel algorithms for fast discovery of associ-ation rules , 1997 .

[24]  James P. Callan,et al.  Automatically labeling hierarchical clusters , 2006, DG.O.

[25]  Henk Barendregt,et al.  The Lambda Calculus: Its Syntax and Semantics , 1985 .

[26]  J. Haldane.  Note on the median of a multivariate distribution , 1948 .

[27]  R. F.,et al.  Mathematical Statistics , 1944, Nature.

[28]  Shamkant B. Navathe,et al.  An Efficient Algorithm for Mining Association Rules in Large Databases , 1995, VLDB.

[29]  Theodor Mader,et al.  Feature Selection with the CLOP Package , 2006 .

[30]  Zhexue Huang,et al.  CLUSTERING LARGE DATA SETS WITH MIXED NUMERIC AND CATEGORICAL VALUES , 1997 .

[31]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.